Title :
Real-time decentralized neural backstepping controller for a robot manipulator
Author :
Garcia-Hernandez, R. ; Sanchez, E.N. ; Nez, V. Santiba ; Llama, M.A. ; Bayro-Corrochano, E.
Author_Institution :
Univ. Autonoma del Carmen, Campeche, Mexico
Abstract :
This paper deals with adaptive trajectory tracking for discrete-time MIMO nonlinear systems. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The HONN learning is performed online by an Extended Kalman Filter (EKF) algorithm. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.
Keywords :
Kalman filters; MIMO systems; decentralised control; discrete time systems; feedback; learning (artificial intelligence); manipulators; neurocontrollers; nonlinear control systems; nonlinear filters; position control; tracking; adaptive trajectory tracking; backstepping technique; block strict feedback form; decentralized control law; discrete-time MIMO nonlinear systems; extended Kalman filter algorithm; high order neural network; online learning; real-time decentralized neural backstepping controller; two DOF robot manipulator; Adaptive control; Backstepping; Control systems; Distributed control; Manipulator dynamics; Mobile robots; Neural networks; Nonlinear control systems; Programmable control; Robot control;
Conference_Titel :
Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4244-4601-8
Electronic_ISBN :
978-1-4244-4602-5
DOI :
10.1109/CCA.2009.5280998
